Turn raw meeting transcripts into assigned actions
prompt.txt
Act as a practical specialist helping me with meeting notes to tasks pipeline. My context: [your meeting types; transcript tool you use; task system (Todoist, Asana, paper); how owners get assigned; weekly meeting count; what falls through the cracks now].
1. Write the extraction prompt: decisions, action items with owners, and open questions as separate lists.
2. Define the task format: verb first, owner, due date, and the meeting it came from.
3. Set routing rules: which tasks go to which system or person automatically.
4. Add the catch-net: a review step that flags ownerless or vague items before they vanish.
5. Close with a weekly digest template: completed, aging, and blocked items per project.
Use my details, not a generic example. If a fact needed for the plan is missing, ask for it or mark the assumption. Keep the result ready to use.
#ai workflows#meeting automation
💡 Replace the [brackets] with your details — the more specific, the sharper the answer.
Sort a crowded inbox without losing the messages that matter.
Act as a practical specialist helping me with inbox triage blueprint. My context: [mail volume, categories, urgent senders, tools available].
1. Define priority tiers and exact inclusion rules.
2. Write example labels and false-positive cases.
3. Design a daily review queue and escalation path.
4. Give a five-message test set with expected labels..
Use my details, not a generic example. If a fact needed for the plan is missing, ask for it or mark the assumption. Keep the result ready to use.
Turn a broad question into a source-checked investigation.
Act as a practical specialist helping me with ai research brief architect. My context: [question, decision it supports, deadline, sources allowed].
1. Break the question into falsifiable subquestions.
2. Set a source hierarchy and recency requirements.
3. Create an evidence table with claim, source, date, confidence.
4. Specify when to stop researching and how to flag gaps..
Use my details, not a generic example. If a fact needed for the plan is missing, ask for it or mark the assumption. Keep the result ready to use.
Make messy documents yield clean, auditable fields.
Act as a practical specialist helping me with document extraction schema. My context: [document types, fields needed, sample document, downstream use].
1. Design a JSON schema with field types and null handling.
2. Write extraction rules with exact quote or page evidence.
3. List ambiguous cases and a human-review threshold.
4. Produce three test cases including a missing field..
Use my details, not a generic example. If a fact needed for the plan is missing, ask for it or mark the assumption. Keep the result ready to use.